-----------------------------------------------------------------------------------------------------------------------
       log:  Z:\interactionmodels\replication\legislativeparties_replication.log
  log type:  text
 opened on:  10 Jan 2007, 20:00:46

. *     ***************************************************************** *;
. *     ***************************************************************** *;
. *       File-Name:      legislativeparties_replication.do               *;
. *       Date:           01/09/2007                                      *;
. *       Author:         MRG                                             *;
. *       Purpose:        Replicate Mozaffar et al. 2003                  *;
. *       Input File:     STATA_mozaffar.dta                              *;
. *       Output File:    legislativeparties_replication.log              *;
. *       Data Output:    none                                            *;
. *       Previous file:                                                  *;
. *       Machine:                                                        *;
. *     ****************************************************************  *;
. *     ****************************************************************  *;
. set mem 10m;
(10240k)

. use getdata\STATA_mozaffar.dta;

. *     ****************************************************************  *;
. *           Summary Statistics                                          *;
. *     ****************************************************************  *;
. sum;

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     country |         0
    year_nyu |        62    1994.081    3.930955       1980       2000
dictator_nyu |        62    .5645161    .4998678          0          1
 elecparties |        62    3.234355    2.699867        1.4       14.9
  legparties |        62    2.340323    1.505894          1        8.8
-------------+--------------------------------------------------------
avemagnitude |        62    32.27841    79.64036          1        400
    logmag10 |        62    .7030161    .7668696          0      2.602
   proximity |        62    .6246774    .4255131          0          1
prescandid~e |        62    2.254839    1.489931          0        7.3
prox_presc~e |        62    1.442323    1.177821          0        4.6
-------------+--------------------------------------------------------
fragmentat~n |        62    4.382419     2.74622          1       9.91
fragmentat~2 |        62    26.62613    28.37423          1      98.21
concentrat~n |        62    1.597258     .963064          0       3.24
   frag_conc |        62    8.193395    6.824679          0    25.4664
  frag2_conc |        62    51.16194    59.53712          0     208.42
-------------+--------------------------------------------------------
logma~2_conc |        62    35.26274    67.12064          0     265.65
         elf |        56      .62625    .2404281          0        .93
       pregb |        62    .4206452     .281692          0        .82
logmag10_elf |        62    .4194211     .602451          0   2.289813
pregb_log~10 |        62    .3253567    .5026586          0   2.003586
-------------+--------------------------------------------------------
      logmag |        62    1.618844      1.7659          0   5.991465
logmag10_c~c |        62    1.173279    1.538905          0    5.31634
logmag10_f~g |        62    3.356764    5.352396          0   20.52978
~0_frag_conc |        62    5.672118    8.517053          0   33.66884
 logmag_conc |        62    2.701537    3.543544          0   12.24354
-------------+--------------------------------------------------------
 logmag_frag |        62    7.729645    12.32561          0   47.27266
logmag_fra~c |        62     13.0607    19.61213          0   77.52715

. *     ****************************************************************  *;
. *       First, try to replicate Model 1 in Table 2, page 386.           *;
. *     ****************************************************************  *;
. regress  legparties logmag10 proximity prox_prescandidate;

      Source |       SS       df       MS              Number of obs =      62
-------------+------------------------------           F(  3,    58) =    5.80
       Model |  31.9083056     3  10.6361019           Prob > F      =  0.0016
    Residual |   106.42249    58  1.83487051           R-squared     =  0.2307
-------------+------------------------------           Adj R-squared =  0.1909
       Total |  138.330795    61  2.26771796           Root MSE      =  1.3546

------------------------------------------------------------------------------
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    logmag10 |   .0491379   .2301636     0.21   0.832    -.4115846    .5098604
   proximity |  -2.789773   .6761694    -4.13   0.000    -4.143273   -1.436273
prox_presc~e |   .8840711   .2451334     3.61   0.001     .3933832    1.374759
       _cons |    2.77337   .3700695     7.49   0.000     2.032596    3.514145
------------------------------------------------------------------------------

. regress  legparties logmag proximity prox_prescandidate;

      Source |       SS       df       MS              Number of obs =      62
-------------+------------------------------           F(  3,    58) =    5.80
       Model |  31.9082513     3  10.6360838           Prob > F      =  0.0016
    Residual |  106.422544    58  1.83487145           R-squared     =  0.2307
-------------+------------------------------           Adj R-squared =  0.1909
       Total |  138.330795    61  2.26771796           Root MSE      =  1.3546

------------------------------------------------------------------------------
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      logmag |    .021332   .0999522     0.21   0.832    -.1787442    .2214081
   proximity |  -2.789774   .6761697    -4.13   0.000    -4.143274   -1.436273
prox_presc~e |   .8840694   .2451334     3.61   0.001     .3933816    1.374757
       _cons |   2.773385   .3700694     7.49   0.000      2.03261    3.514159
------------------------------------------------------------------------------

. regress  legparties logmag10 proximity prox_prescandidate, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  3,    58) =    7.46
                                                       Prob > F      =  0.0003
                                                       R-squared     =  0.2307
                                                       Root MSE      =  1.3546

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    logmag10 |   .0491379   .1356965     0.36   0.719    -.2224881    .3207638
   proximity |  -2.789773   .6380585    -4.37   0.000    -4.066986    -1.51256
prox_presc~e |   .8840711   .2090459     4.23   0.000     .4656202    1.302522
       _cons |    2.77337   .4688689     5.92   0.000     1.834827    3.711914
------------------------------------------------------------------------------

. regress  legparties logmag proximity prox_prescandidate, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  3,    58) =    7.46
                                                       Prob > F      =  0.0003
                                                       R-squared     =  0.2307
                                                       Root MSE      =  1.3546

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      logmag |    .021332   .0589269     0.36   0.719     -.096623     .139287
   proximity |  -2.789774   .6380603    -4.37   0.000     -4.06699   -1.512557
prox_presc~e |   .8840694   .2090458     4.23   0.000     .4656188     1.30252
       _cons |   2.773385   .4688815     5.91   0.000     1.834816    3.711953
------------------------------------------------------------------------------

. *     ****************************************************************  *;
. *       Neither of these work                                           *;
. *     ****************************************************************  *;
. *     ****************************************************************  *;
. *       Model 2                                                         *;
. *     ****************************************************************  *;
. regress legparties fragmentation concentration frag_conc;

      Source |       SS       df       MS              Number of obs =      62
-------------+------------------------------           F(  3,    58) =   12.93
       Model |  55.4272508     3  18.4757503           Prob > F      =  0.0000
    Residual |  82.9035445    58  1.42937146           R-squared     =  0.4007
-------------+------------------------------           Adj R-squared =  0.3697
       Total |  138.330795    61  2.26771796           Root MSE      =  1.1956

------------------------------------------------------------------------------
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
fragmentat~n |  -.3472055   .1101943    -3.15   0.003    -.5677833   -.1266277
concentrat~n |  -.1966835   .2668285    -0.74   0.464    -.7307989    .3374318
   frag_conc |   .2558319   .0595583     4.30   0.000     .1366131    .3750507
       _cons |   2.079945   .3987027     5.22   0.000     1.281855    2.878035
------------------------------------------------------------------------------

. regress legparties fragmentation concentration frag_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  3,    58) =    6.81
                                                       Prob > F      =  0.0005
                                                       R-squared     =  0.4007
                                                       Root MSE      =  1.1956

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
fragmentat~n |  -.3472055   .1235835    -2.81   0.007    -.5945847   -.0998263
concentrat~n |  -.1966835   .2669845    -0.74   0.464    -.7311111     .337744
   frag_conc |   .2558319   .0837544     3.05   0.003     .0881793    .4234845
       _cons |   2.079945   .2306208     9.02   0.000     1.618308    2.541583
------------------------------------------------------------------------------

. regress legparties fragmentation2 concentration frag_conc;

      Source |       SS       df       MS              Number of obs =      62
-------------+------------------------------           F(  3,    58) =   10.53
       Model |  48.7831366     3  16.2610455           Prob > F      =  0.0000
    Residual |  89.5476587    58  1.54392515           R-squared     =  0.3527
-------------+------------------------------           Adj R-squared =  0.3192
       Total |  138.330795    61  2.26771796           Root MSE      =  1.2425

------------------------------------------------------------------------------
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
fragmentat~2 |  -.0231894   .0104889    -2.21   0.031    -.0441852   -.0021935
concentrat~n |  -.2169059    .304676    -0.71   0.479    -.8267811    .3929693
   frag_conc |   .2154692   .0624248     3.45   0.001     .0905123    .3404261
       _cons |   1.538796   .3363132     4.58   0.000     .8655925       2.212
------------------------------------------------------------------------------

. regress legparties fragmentation2 concentration frag_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  3,    58) =    6.10
                                                       Prob > F      =  0.0011
                                                       R-squared     =  0.3527
                                                       Root MSE      =  1.2425

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
fragmentat~2 |  -.0231894   .0108321    -2.14   0.037    -.0448721   -.0015066
concentrat~n |  -.2169059   .3234011    -0.67   0.505    -.8642635    .4304516
   frag_conc |   .2154692   .0898954     2.40   0.020      .035524    .3954145
       _cons |   1.538796   .1577479     9.75   0.000      1.22303    1.854563
------------------------------------------------------------------------------

. regress legparties fragmentation2 concentration frag2_conc;

      Source |       SS       df       MS              Number of obs =      62
-------------+------------------------------           F(  3,    58) =   12.75
       Model |  54.9693612     3  18.3231204           Prob > F      =  0.0000
    Residual |  83.3614341    58  1.43726611           R-squared     =  0.3974
-------------+------------------------------           Adj R-squared =  0.3662
       Total |  138.330795    61  2.26771796           Root MSE      =  1.1989

------------------------------------------------------------------------------
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
fragmentat~2 |  -.0329767   .0111651    -2.95   0.005    -.0553262   -.0106272
concentrat~n |   .1639454   .2046322     0.80   0.426    -.2456704    .5735612
  frag2_conc |   .0250271   .0060518     4.14   0.000     .0129131     .037141
       _cons |   1.676069   .3311658     5.06   0.000     1.013168    2.338969
------------------------------------------------------------------------------

. regress legparties fragmentation2 concentration frag2_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  3,    58) =    6.67
                                                       Prob > F      =  0.0006
                                                       R-squared     =  0.3974
                                                       Root MSE      =  1.1989

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
fragmentat~2 |  -.0329767   .0126559    -2.61   0.012    -.0583102   -.0076432
concentrat~n |   .1639454   .1601838     1.02   0.310    -.1566972     .484588
  frag2_conc |   .0250271    .008525     2.94   0.005     .0079625    .0420917
       _cons |   1.676069    .153695    10.91   0.000     1.368415    1.983723
------------------------------------------------------------------------------

. regress legparties fragmentation fragmentation2 concentration frag2_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  4,    57) =    4.80
                                                       Prob > F      =  0.0021
                                                       R-squared     =  0.4202
                                                       Root MSE      =  1.1863

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
fragmentat~n |  -.4184839   .3271467    -1.28   0.206    -1.073584     .236616
fragmentat~2 |   .0062964   .0318939     0.20   0.844      -.05757    .0701628
concentrat~n |    .388199   .2844186     1.36   0.178    -.1813394    .9577373
  frag2_conc |   .0236103   .0084741     2.79   0.007     .0066412    .0405794
       _cons |   2.178643   .4129334     5.28   0.000     1.351758    3.005528
------------------------------------------------------------------------------

. regress legparties fragmentation fragmentation2 concentration frag_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  4,    57) =    5.02
                                                       Prob > F      =  0.0016
                                                       R-squared     =  0.4250
                                                       Root MSE      =  1.1813

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
fragmentat~n |  -.7441366   .3278166    -2.27   0.027    -1.400578   -.0876952
fragmentat~2 |   .0395133   .0290935     1.36   0.180    -.0187454     .097772
concentrat~n |   .0428573   .3682228     0.12   0.908     -.694496    .7802107
   frag_conc |   .2380957   .0827804     2.88   0.006     .0723308    .4038605
       _cons |   2.530089   .4117784     6.14   0.000     1.705517    3.354661
------------------------------------------------------------------------------

. *     ****************************************************************  *;
. *       Could not replicate either, but closest one was with            *;
. *       fragmentation2 and frag2_conc.                                  *;
. *     ****************************************************************  *;
. *     ****************************************************************  *;
. *       Model 3                                                         *;
. *     ****************************************************************  *;
. regress legparties logmag10 proximity prox_prescandidate fragmentation concentration frag_conc;

      Source |       SS       df       MS              Number of obs =      62
-------------+------------------------------           F(  6,    55) =   10.69
       Model |  74.4596336     6  12.4099389           Prob > F      =  0.0000
    Residual |  63.8711617    55  1.16129385           R-squared     =  0.5383
-------------+------------------------------           Adj R-squared =  0.4879
       Total |  138.330795    61  2.26771796           Root MSE      =  1.0776

------------------------------------------------------------------------------
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    logmag10 |   .2471074   .1998563     1.24   0.222    -.1534136    .6476284
   proximity |  -2.045573   .5555527    -3.68   0.001    -3.158925   -.9322205
prox_presc~e |   .5569168   .2047094     2.72   0.009     .1466701    .9671635
fragmentat~n |  -.3327925   .1088338    -3.06   0.003    -.5509003   -.1146847
concentrat~n |   -.292459   .2535778    -1.15   0.254    -.8006403    .2157224
   frag_conc |   .2519013   .0582926     4.32   0.000     .1350803    .3687223
       _cons |   2.502813   .3966185     6.31   0.000     1.707972    3.297654
------------------------------------------------------------------------------

. regress legparties logmag10 proximity prox_prescandidate fragmentation concentration frag_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  6,    55) =    8.11
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.5383
                                                       Root MSE      =  1.0776

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    logmag10 |   .2471074   .1903958     1.30   0.200    -.1344543     .628669
   proximity |  -2.045573   .4918599    -4.16   0.000    -3.031282   -1.059864
prox_presc~e |   .5569168   .1701158     3.27   0.002     .2159972    .8978365
fragmentat~n |  -.3327925   .1189335    -2.80   0.007    -.5711405   -.0944445
concentrat~n |   -.292459   .2938945    -1.00   0.324    -.8814367    .2965188
   frag_conc |   .2519013   .0846728     2.97   0.004     .0822132    .4215894
       _cons |   2.502813   .3527212     7.10   0.000     1.795944    3.209682
------------------------------------------------------------------------------

. regress legparties logmag proximity prox_prescandidate fragmentation concentration frag_conc;

      Source |       SS       df       MS              Number of obs =      62
-------------+------------------------------           F(  6,    55) =   10.69
       Model |  74.4603831     6  12.4100638           Prob > F      =  0.0000
    Residual |  63.8704122    55  1.16128022           R-squared     =  0.5383
-------------+------------------------------           Adj R-squared =  0.4879
       Total |  138.330795    61  2.26771796           Root MSE      =  1.0776

------------------------------------------------------------------------------
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      logmag |   .1073312   .0867889     1.24   0.221    -.0665976      .28126
   proximity |  -2.045543   .5555507    -3.68   0.001    -3.158891   -.9321942
prox_presc~e |   .5569116   .2047076     2.72   0.009     .1466685    .9671547
fragmentat~n |  -.3328042   .1088335    -3.06   0.003    -.5509114   -.1146971
concentrat~n |  -.2924431   .2535677    -1.15   0.254     -.800604    .2157179
   frag_conc |    .251905   .0582917     4.32   0.000     .1350858    .3687241
       _cons |   2.502766   .3966201     6.31   0.000     1.707922     3.29761
------------------------------------------------------------------------------

. regress legparties logmag proximity prox_prescandidate fragmentation concentration frag_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  6,    55) =    8.11
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.5383
                                                       Root MSE      =  1.0776

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      logmag |   .1073312   .0826803     1.30   0.200    -.0583637    .2730262
   proximity |  -2.045543   .4918591    -4.16   0.000     -3.03125   -1.059835
prox_presc~e |   .5569116   .1701129     3.27   0.002     .2159978    .8978254
fragmentat~n |  -.3328042   .1189314    -2.80   0.007    -.5711481   -.0944603
concentrat~n |  -.2924431   .2938728    -1.00   0.324    -.8813774    .2964913
   frag_conc |    .251905   .0846706     2.98   0.004     .0822213    .4215886
       _cons |   2.502766   .3527189     7.10   0.000     1.795902    3.209631
------------------------------------------------------------------------------

. regress legparties logmag10 proximity prox_prescandidate fragmentation2 concentration frag_conc;

      Source |       SS       df       MS              Number of obs =      62
-------------+------------------------------           F(  6,    55) =    9.58
       Model |  70.6907943     6  11.7817991           Prob > F      =  0.0000
    Residual |   67.640001    55   1.2298182           R-squared     =  0.5110
-------------+------------------------------           Adj R-squared =  0.4577
       Total |  138.330795    61  2.26771796           Root MSE      =   1.109

------------------------------------------------------------------------------
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    logmag10 |   .2237471   .2091226     1.07   0.289     -.195344    .6428383
   proximity |  -2.213536     .56531    -3.92   0.000    -3.346442   -1.080629
prox_presc~e |   .5737558   .2104497     2.73   0.009     .1520051    .9955065
fragmentat~2 |   -.024792   .0103258    -2.40   0.020    -.0454854   -.0040985
concentrat~n |  -.3523249   .2921759    -1.21   0.233    -.9378586    .2332087
   frag_conc |   .2259175   .0610736     3.70   0.001     .1035233    .3483118
       _cons |   2.110066   .3711133     5.69   0.000     1.366339    2.853794
------------------------------------------------------------------------------

. regress legparties logmag10 proximity prox_prescandidate fragmentation2 concentration frag_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  6,    55) =    7.10
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.5110
                                                       Root MSE      =   1.109

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    logmag10 |   .2237471    .224493     1.00   0.323     -.226147    .6736412
   proximity |  -2.213536   .5140441    -4.31   0.000    -3.243703   -1.183369
prox_presc~e |   .5737558   .1721396     3.33   0.002     .2287802    .9187313
fragmentat~2 |   -.024792    .012563    -1.97   0.053    -.0499687    .0003847
concentrat~n |  -.3523249    .371905    -0.95   0.348    -1.097639    .3929893
   frag_conc |   .2259175   .0969477     2.33   0.023       .03163    .4202051
       _cons |   2.110066    .323576     6.52   0.000     1.461606    2.758527
------------------------------------------------------------------------------

. regress legparties logmag10 proximity prox_prescandidate fragmentation2 concentration frag2_conc;

      Source |       SS       df       MS              Number of obs =      62
-------------+------------------------------           F(  6,    55) =   10.31
       Model |  73.2300291     6  12.2050049           Prob > F      =  0.0000
    Residual |  65.1007662    55  1.18365029           R-squared     =  0.5294
-------------+------------------------------           Adj R-squared =  0.4780
       Total |  138.330795    61  2.26771796           Root MSE      =   1.088

------------------------------------------------------------------------------
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    logmag10 |   .2318577   .2038056     1.14   0.260    -.1765778    .6402933
   proximity |  -2.003577   .5614902    -3.57   0.001    -3.128828   -.8783255
prox_presc~e |   .5328925   .2073445     2.57   0.013     .1173648    .9484202
fragmentat~2 |  -.0322411   .0112208    -2.87   0.006    -.0547281   -.0097541
concentrat~n |   .0875656   .1953787     0.45   0.656    -.3039821    .4791133
  frag2_conc |   .0243793    .006027     4.05   0.000      .012301    .0364577
       _cons |   2.131604    .364345     5.85   0.000     1.401441    2.861768
------------------------------------------------------------------------------

. regress legparties logmag10 proximity prox_prescandidate fragmentation2 concentration frag2_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  6,    55) =    7.08
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.5294
                                                       Root MSE      =   1.088

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    logmag10 |   .2318577   .1875218     1.24   0.222    -.1439443    .6076598
   proximity |  -2.003577   .4975088    -4.03   0.000    -3.000607   -1.006547
prox_presc~e |   .5328925   .1831794     2.91   0.005     .1657927    .8999923
fragmentat~2 |  -.0322411    .013026    -2.48   0.016    -.0583457   -.0061364
concentrat~n |   .0875656   .1877622     0.47   0.643    -.2887183    .4638496
  frag2_conc |   .0243793   .0087669     2.78   0.007     .0068102    .0419485
       _cons |   2.131604   .3066587     6.95   0.000     1.517047    2.746162
------------------------------------------------------------------------------

. regress legparties logmag proximity prox_prescandidate fragmentation2 concentration frag_conc;

      Source |       SS       df       MS              Number of obs =      62
-------------+------------------------------           F(  6,    55) =    9.58
       Model |  70.6915011     6  11.7819168           Prob > F      =  0.0000
    Residual |  67.6392942    55  1.22980535           R-squared     =  0.5110
-------------+------------------------------           Adj R-squared =  0.4577
       Total |  138.330795    61  2.26771796           Root MSE      =   1.109

------------------------------------------------------------------------------
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      logmag |   .0971888   .0908132     1.07   0.289    -.0848048    .2791825
   proximity |  -2.213511   .5653079    -3.92   0.000    -3.346413   -1.080608
prox_presc~e |   .5737518    .210448     2.73   0.009     .1520046     .995499
fragmentat~2 |  -.0247933   .0103258    -2.40   0.020    -.0454867   -.0040998
concentrat~n |  -.3523225    .292164    -1.21   0.233    -.9378323    .2331873
   frag_conc |   .2259227   .0610728     3.70   0.001     .1035301    .3483152
       _cons |    2.11001   .3711189     5.69   0.000     1.366271    2.853749
------------------------------------------------------------------------------

. regress legparties logmag proximity prox_prescandidate fragmentation2 concentration frag_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  6,    55) =    7.10
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.5110
                                                       Root MSE      =   1.109

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      logmag |   .0971888   .0974868     1.00   0.323    -.0981791    .2925568
   proximity |  -2.213511   .5140448    -4.31   0.000    -3.243679   -1.183342
prox_presc~e |   .5737518   .1721363     3.33   0.002     .2287829    .9187207
fragmentat~2 |  -.0247933   .0125629    -1.97   0.053    -.0499698    .0003833
concentrat~n |  -.3523225   .3718773    -0.95   0.348    -1.097581    .3929362
   frag_conc |   .2259227   .0969457     2.33   0.023     .0316392    .4202061
       _cons |    2.11001   .3235825     6.52   0.000     1.461536    2.758483
------------------------------------------------------------------------------

. regress legparties logmag proximity prox_prescandidate fragmentation2 concentration frag2_conc;

      Source |       SS       df       MS              Number of obs =      62
-------------+------------------------------           F(  6,    55) =   10.31
       Model |  73.2308122     6  12.2051354           Prob > F      =  0.0000
    Residual |  65.0999831    55  1.18363606           R-squared     =  0.5294
-------------+------------------------------           Adj R-squared =  0.4780
       Total |  138.330795    61  2.26771796           Root MSE      =   1.088

------------------------------------------------------------------------------
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      logmag |   .1007126   .0885043     1.14   0.260    -.0766541    .2780793
   proximity |  -2.003546   .5614881    -3.57   0.001    -3.128793   -.8782987
prox_presc~e |   .5328877   .2073428     2.57   0.013     .1173635    .9484119
fragmentat~2 |  -.0322426   .0112208    -2.87   0.006    -.0547296   -.0097556
concentrat~n |   .0875784   .1953709     0.45   0.656    -.3039536    .4791105
  frag2_conc |   .0243799   .0060269     4.05   0.000     .0123017    .0364581
       _cons |   2.131545   .3643502     5.85   0.000     1.401371    2.861719
------------------------------------------------------------------------------

. regress legparties logmag proximity prox_prescandidate fragmentation2 concentration frag2_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  6,    55) =    7.08
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.5294
                                                       Root MSE      =   1.088

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      logmag |   .1007126   .0814339     1.24   0.221    -.0624846    .2639098
   proximity |  -2.003546    .497508    -4.03   0.000    -3.000574   -1.006518
prox_presc~e |   .5328877    .183176     2.91   0.005     .1657948    .8999806
fragmentat~2 |  -.0322426   .0130259    -2.48   0.016     -.058347   -.0061381
concentrat~n |   .0875784    .187748     0.47   0.643     -.288677    .4638339
  frag2_conc |   .0243799   .0087667     2.78   0.007     .0068111    .0419487
       _cons |   2.131545   .3066626     6.95   0.000     1.516979     2.74611
------------------------------------------------------------------------------

. regress legparties logmag10 proximity prox_prescandidate fragmentation2 concentration frag2_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  6,    55) =    7.08
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.5294
                                                       Root MSE      =   1.088

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    logmag10 |   .2318577   .1875218     1.24   0.222    -.1439443    .6076598
   proximity |  -2.003577   .4975088    -4.03   0.000    -3.000607   -1.006547
prox_presc~e |   .5328925   .1831794     2.91   0.005     .1657927    .8999923
fragmentat~2 |  -.0322411    .013026    -2.48   0.016    -.0583457   -.0061364
concentrat~n |   .0875656   .1877622     0.47   0.643    -.2887183    .4638496
  frag2_conc |   .0243793   .0087669     2.78   0.007     .0068102    .0419485
       _cons |   2.131604   .3066587     6.95   0.000     1.517047    2.746162
------------------------------------------------------------------------------

. *     ****************************************************************  *;
. *       Could not replicate - but last equation is closest.             *;
. *     ****************************************************************  *;
. *     ****************************************************************  *;
. *                               Model 4                                 *;
. *     ****************************************************************  *;
. regress  legparties logmag10 proximity prox_prescandidate fragmentation concentration logmag10_frag_conc;

      Source |       SS       df       MS              Number of obs =      62
-------------+------------------------------           F(  6,    55) =    9.69
       Model |   71.096009     6  11.8493348           Prob > F      =  0.0000
    Residual |  67.2347864    55  1.22245066           R-squared     =  0.5140
-------------+------------------------------           Adj R-squared =  0.4609
       Total |  138.330795    61  2.26771796           Root MSE      =  1.1056

------------------------------------------------------------------------------
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    logmag10 |   -1.31052   .3736643    -3.51   0.001     -2.05936   -.5616801
   proximity |  -1.819948   .5848277    -3.11   0.003    -2.991969   -.6479271
prox_presc~e |     .60979    .209279     2.91   0.005     .1903855    1.029195
fragmentat~n |   -.084942   .0708081    -1.20   0.235    -.2268445    .0569605
concentrat~n |   .3488599   .1775692     1.96   0.055    -.0069968    .7047166
~0_frag_conc |   .1534308   .0396314     3.87   0.000     .0740077    .2328539
       _cons |   2.463761   .4082522     6.03   0.000     1.645605    3.281916
------------------------------------------------------------------------------

. regress  legparties logmag10 proximity prox_prescandidate fragmentation concentration logmag10_frag_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  6,    55) =    7.47
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.5140
                                                       Root MSE      =  1.1056

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    logmag10 |   -1.31052   .5060426    -2.59   0.012    -2.324652   -.2963882
   proximity |  -1.819948   .4518114    -4.03   0.000    -2.725398   -.9144978
prox_presc~e |     .60979   .1550929     3.93   0.000      .298977    .9206031
fragmentat~n |   -.084942   .0524566    -1.62   0.111    -.1900675    .0201834
concentrat~n |   .3488599   .1827551     1.91   0.061    -.0173895    .7151093
~0_frag_conc |   .1534308   .0606274     2.53   0.014     .0319308    .2749308
       _cons |   2.463761   .4230875     5.82   0.000     1.615874    3.311647
------------------------------------------------------------------------------

. regress  legparties logmag10 proximity prox_prescandidate fragmentation2 concentration logmag10_frag_conc;

      Source |       SS       df       MS              Number of obs =      62
-------------+------------------------------           F(  6,    55) =    9.38
       Model |  69.9659983     6  11.6609997           Prob > F      =  0.0000
    Residual |   68.364797    55  1.24299631           R-squared     =  0.5058
-------------+------------------------------           Adj R-squared =  0.4519
       Total |  138.330795    61  2.26771796           Root MSE      =  1.1149

------------------------------------------------------------------------------
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    logmag10 |  -1.228199   .3725022    -3.30   0.002     -1.97471   -.4816876
   proximity |  -1.902588   .5837181    -3.26   0.002    -3.072385   -.7327907
prox_presc~e |   .6118393   .2110436     2.90   0.005     .1888985     1.03478
fragmentat~2 |   -.004552   .0063981    -0.71   0.480    -.0173742    .0082701
concentrat~n |   .3102901   .1756822     1.77   0.083    -.0417848     .662365
~0_frag_conc |   .1423347   .0395448     3.60   0.001     .0630853    .2215842
       _cons |    2.32805   .3866581     6.02   0.000      1.55317     3.10293
------------------------------------------------------------------------------

. regress  legparties logmag10 proximity prox_prescandidate fragmentation2 concentration logmag10_frag_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  6,    55) =    7.05
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.5058
                                                       Root MSE      =  1.1149

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    logmag10 |  -1.228199   .4903679    -2.50   0.015    -2.210918   -.2454796
   proximity |  -1.902588   .4612353    -4.12   0.000    -2.826924   -.9782515
prox_presc~e |   .6118393   .1594011     3.84   0.000     .2923923    .9312864
fragmentat~2 |   -.004552    .003467    -1.31   0.195    -.0115001    .0023961
concentrat~n |   .3102901   .1730367     1.79   0.078    -.0364833    .6570634
~0_frag_conc |   .1423347   .0603501     2.36   0.022     .0213905     .263279
       _cons |    2.32805   .3835584     6.07   0.000     1.559382    3.096718
------------------------------------------------------------------------------

. regress  legparties logmag10 proximity prox_prescandidate fragmentation2 concentration logmag10_frag2_conc;

      Source |       SS       df       MS              Number of obs =      62
-------------+------------------------------           F(  6,    55) =    9.68
       Model |  71.0663431     6  11.8443905           Prob > F      =  0.0000
    Residual |  67.2644522    55  1.22299004           R-squared     =  0.5137
-------------+------------------------------           Adj R-squared =  0.4607
       Total |  138.330795    61  2.26771796           Root MSE      =  1.1059

------------------------------------------------------------------------------
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    logmag10 |  -.8310841   .2768064    -3.00   0.004    -1.385817   -.2763516
   proximity |  -1.898329   .5781331    -3.28   0.002    -3.056933    -.739724
prox_presc~e |   .6506833   .2093113     3.11   0.003     .2312141    1.070153
fragmentat~2 |  -.0120115   .0075259    -1.60   0.116    -.0270937    .0030707
concentrat~n |   .5189957   .1617628     3.21   0.002     .1948159    .8431756
logma~2_conc |   .0157903   .0042101     3.75   0.000     .0073531    .0242275
       _cons |   2.105977   .3698509     5.69   0.000      1.36478    2.847175
------------------------------------------------------------------------------

. regress  legparties logmag10 proximity prox_prescandidate fragmentation2 concentration logmag10_frag2_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  6,    55) =    6.98
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.5137
                                                       Root MSE      =  1.1059

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    logmag10 |  -.8310841   .2998111    -2.77   0.008    -1.431919   -.2302492
   proximity |  -1.898329   .4460474    -4.26   0.000    -2.792228    -1.00443
prox_presc~e |   .6506833   .1614738     4.03   0.000     .3270826     .974284
fragmentat~2 |  -.0120115   .0056226    -2.14   0.037    -.0232794   -.0007437
concentrat~n |   .5189957   .1752602     2.96   0.005     .1677664    .8702251
logma~2_conc |   .0157903   .0061742     2.56   0.013     .0034168    .0281637
       _cons |   2.105977   .3187379     6.61   0.000     1.467212    2.744742
------------------------------------------------------------------------------

. regress  legparties logmag proximity prox_prescandidate fragmentation concentration logmag_frag_conc;

      Source |       SS       df       MS              Number of obs =      62
-------------+------------------------------           F(  6,    55) =    9.69
       Model |  71.0894128     6  11.8482355           Prob > F      =  0.0000
    Residual |  67.2413825    55  1.22257059           R-squared     =  0.5139
-------------+------------------------------           Adj R-squared =  0.4609
       Total |  138.330795    61  2.26771796           Root MSE      =  1.1057

------------------------------------------------------------------------------
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      logmag |  -.5690114   .1622815    -3.51   0.001    -.8942308   -.2437921
   proximity |  -1.820101   .5848536    -3.11   0.003    -2.992174   -.6480283
prox_presc~e |   .6098157   .2092883     2.91   0.005     .1903926    1.029239
fragmentat~n |  -.0849235   .0708127    -1.20   0.236    -.2268353    .0569884
concentrat~n |   .3488241    .177581     1.96   0.055    -.0070561    .7047043
logmag_fra~c |   .0666214   .0172122     3.87   0.000     .0321274    .1011154
       _cons |   2.463774    .408294     6.03   0.000     1.645534    3.282013
------------------------------------------------------------------------------

. regress  legparties logmag proximity prox_prescandidate fragmentation concentration logmag_frag_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  6,    55) =    7.47
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.5139
                                                       Root MSE      =  1.1057

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      logmag |  -.5690114    .219769    -2.59   0.012    -1.009438   -.1285845
   proximity |  -1.820101   .4518236    -4.03   0.000    -2.725576   -.9146265
prox_presc~e |   .6098157   .1551022     3.93   0.000     .2989838    .9206475
fragmentat~n |  -.0849235   .0524582    -1.62   0.111    -.1900519     .020205
concentrat~n |   .3488241   .1827615     1.91   0.062     -.017438    .7150863
logmag_fra~c |   .0666214    .026331     2.53   0.014      .013853    .1193899
       _cons |   2.463774   .4231402     5.82   0.000     1.615782    3.311766
------------------------------------------------------------------------------

. regress  legparties logmag proximity prox_prescandidate fragmentation2 concentration logmag_frag_conc;

      Source |       SS       df       MS              Number of obs =      62
-------------+------------------------------           F(  6,    55) =    9.38
       Model |    69.95972     6  11.6599533           Prob > F      =  0.0000
    Residual |  68.3710753    55  1.24311046           R-squared     =  0.5057
-------------+------------------------------           Adj R-squared =  0.4518
       Total |  138.330795    61  2.26771796           Root MSE      =  1.1149

------------------------------------------------------------------------------
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      logmag |  -.5332615   .1617759    -3.30   0.002    -.8574676   -.2090554
   proximity |  -1.902732   .5837421    -3.26   0.002    -3.072577   -.7328867
prox_presc~e |   .6118635   .2110523     2.90   0.005     .1889052    1.034822
fragmentat~2 |  -.0045502   .0063985    -0.71   0.480    -.0173732    .0082727
concentrat~n |   .3102669   .1756946     1.77   0.083     -.041833    .6623667
logmag_fra~c |   .0618024   .0171745     3.60   0.001      .027384    .0962208
       _cons |   2.328075   .3866953     6.02   0.000      1.55312     3.10303
------------------------------------------------------------------------------

. regress  legparties logmag proximity prox_prescandidate fragmentation2 concentration logmag_frag_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  6,    55) =    7.05
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.5057
                                                       Root MSE      =  1.1149

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      logmag |  -.5332615   .2129622    -2.50   0.015    -.9600473   -.1064757
   proximity |  -1.902732   .4612437    -4.13   0.000    -2.827085    -.978379
prox_presc~e |   .6118635   .1594097     3.84   0.000     .2923994    .9313276
fragmentat~2 |  -.0045502   .0034672    -1.31   0.195    -.0114986    .0023982
concentrat~n |   .3102669   .1730463     1.79   0.078    -.0365256    .6570593
logmag_fra~c |   .0618024   .0262104     2.36   0.022     .0092756    .1143292
       _cons |   2.328075   .3836008     6.07   0.000     1.559322    3.096828
------------------------------------------------------------------------------

. gen logmag_frag2_conc = logmag*fragmentation2*concentration;

. label var logmag_frag2_conc "logmag*fragmentation2*concentration";

. regress  legparties logmag proximity prox_prescandidate fragmentation2 concentration logmag_frag2_conc;

      Source |       SS       df       MS              Number of obs =      62
-------------+------------------------------           F(  6,    55) =    9.68
       Model |  71.0647043     6  11.8441174           Prob > F      =  0.0000
    Residual |   67.266091    55  1.22301984           R-squared     =  0.5137
-------------+------------------------------           Adj R-squared =  0.4607
       Total |  138.330795    61  2.26771796           Root MSE      =  1.1059

------------------------------------------------------------------------------
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      logmag |  -.3608849   .1202112    -3.00   0.004    -.6017935   -.1199763
   proximity |  -1.898427   .5781356    -3.28   0.002    -3.057036   -.7398169
prox_presc~e |   .6507178   .2093131     3.11   0.003      .231245    1.070191
fragmentat~2 |  -.0120096   .0075258    -1.60   0.116    -.0270915    .0030724
concentrat~n |   .5188881   .1617646     3.21   0.002     .1947047    .8430716
logmag_fra.. |   .0068575   .0018285     3.75   0.000     .0031931    .0105218
       _cons |    2.10605   .3698682     5.69   0.000     1.364818    2.847283
------------------------------------------------------------------------------

. regress  legparties logmag proximity prox_prescandidate fragmentation2 concentration logmag_frag2_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  6,    55) =    6.98
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.5137
                                                       Root MSE      =  1.1059

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      logmag |  -.3608849   .1301993    -2.77   0.008    -.6218102   -.0999596
   proximity |  -1.898427   .4460532    -4.26   0.000    -2.792337   -1.004516
prox_presc~e |   .6507178   .1614756     4.03   0.000     .3271135    .9743221
fragmentat~2 |  -.0120096   .0056221    -2.14   0.037    -.0232766   -.0007426
concentrat~n |   .5188881   .1752463     2.96   0.005     .1676867    .8700896
logmag_fra.. |   .0068575   .0026815     2.56   0.013     .0014836    .0122313
       _cons |    2.10605   .3187629     6.61   0.000     1.467235    2.744865
------------------------------------------------------------------------------

. *     ****************************************************************  *;
. *      Again, not possible to replicate despite multiple configurations *;
. *     ****************************************************************  *;
. *     ****************************************************************  *;
. *     ****************************************************************  *;
. *     ****************************************************************  *;
. *       These are the specifications that get us the closest to the     *;
. *       results shown in Table 1.                                       *;
. *     ****************************************************************  *;
. *     ****************************************************************  *;
. *     ****************************************************************  *;
. *     ****************************************************************  *;
. *                               Model 1                                 *;
. *     ****************************************************************  *;
. regress legparties logmag10 proximity prox_prescandidate, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  3,    58) =    7.46
                                                       Prob > F      =  0.0003
                                                       R-squared     =  0.2307
                                                       Root MSE      =  1.3546

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    logmag10 |   .0491379   .1356965     0.36   0.719    -.2224881    .3207638
   proximity |  -2.789773   .6380585    -4.37   0.000    -4.066986    -1.51256
prox_presc~e |   .8840711   .2090459     4.23   0.000     .4656202    1.302522
       _cons |    2.77337   .4688689     5.92   0.000     1.834827    3.711914
------------------------------------------------------------------------------

. *     ****************************************************************  *;
. *                               Model 2                                 *;
. *     ****************************************************************  *;
. regress legparties fragmentation2 concentration frag2_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  3,    58) =    6.67
                                                       Prob > F      =  0.0006
                                                       R-squared     =  0.3974
                                                       Root MSE      =  1.1989

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
fragmentat~2 |  -.0329767   .0126559    -2.61   0.012    -.0583102   -.0076432
concentrat~n |   .1639454   .1601838     1.02   0.310    -.1566972     .484588
  frag2_conc |   .0250271    .008525     2.94   0.005     .0079625    .0420917
       _cons |   1.676069    .153695    10.91   0.000     1.368415    1.983723
------------------------------------------------------------------------------

. *     ****************************************************************  *;
. *                               Model 3                                 *;
. *     ****************************************************************  *;
. regress legparties logmag10 proximity prox_prescandidate fragmentation2 concentration frag2_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  6,    55) =    7.08
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.5294
                                                       Root MSE      =   1.088

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    logmag10 |   .2318577   .1875218     1.24   0.222    -.1439443    .6076598
   proximity |  -2.003577   .4975088    -4.03   0.000    -3.000607   -1.006547
prox_presc~e |   .5328925   .1831794     2.91   0.005     .1657927    .8999923
fragmentat~2 |  -.0322411    .013026    -2.48   0.016    -.0583457   -.0061364
concentrat~n |   .0875656   .1877622     0.47   0.643    -.2887183    .4638496
  frag2_conc |   .0243793   .0087669     2.78   0.007     .0068102    .0419485
       _cons |   2.131604   .3066587     6.95   0.000     1.517047    2.746162
------------------------------------------------------------------------------

. *     ****************************************************************  *;
. *                               Model 4                                 *;
. *     ****************************************************************  *;
. regress legparties logmag10 proximity prox_prescandidate fragmentation2 concentration logmag10_frag2_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  6,    55) =    6.98
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.5137
                                                       Root MSE      =  1.1059

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    logmag10 |  -.8310841   .2998111    -2.77   0.008    -1.431919   -.2302492
   proximity |  -1.898329   .4460474    -4.26   0.000    -2.792228    -1.00443
prox_presc~e |   .6506833   .1614738     4.03   0.000     .3270826     .974284
fragmentat~2 |  -.0120115   .0056226    -2.14   0.037    -.0232794   -.0007437
concentrat~n |   .5189957   .1752602     2.96   0.005     .1677664    .8702251
logmag10_f.. |   .0157903   .0061742     2.56   0.013     .0034168    .0281637
       _cons |   2.105977   .3187379     6.61   0.000     1.467212    2.744742
------------------------------------------------------------------------------

. *     ****************************************************************  *;
. *     ****************************************************************  *;
. *     ****************************************************************  *;
. *     ****************************************************************  *;
. *                       Correct specifications                          *;
. *     ****************************************************************  *;
. *     ****************************************************************  *;
. *     ****************************************************************  *;
. *     ****************************************************************  *;
. *     ****************************************************************  *;
. *                               Model 1                                 *;
. *     ****************************************************************  *;
. regress legparties logmag10 proximity prescandidate prox_prescandidate, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  4,    57) =    7.32
                                                       Prob > F      =  0.0001
                                                       R-squared     =  0.4516
                                                       Root MSE      =  1.1536

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    logmag10 |   .1733719   .1168389     1.48   0.143     -.060594    .4073378
   proximity |   -1.22142   .4539332    -2.69   0.009    -2.130405   -.3124342
prescandid~e |   .6007957   .1848137     3.25   0.002     .2307128    .9708787
prox_presc~e |   .1388049   .2913628     0.48   0.636    -.4446391    .7222489
       _cons |   1.426534   .2343787     6.09   0.000     .9571986    1.895869
------------------------------------------------------------------------------

. *     ****************************************************************  *;
. *                               Model 2                                 *;
. *     ****************************************************************  *;
. regress legparties fragmentation concentration frag_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  3,    58) =    6.81
                                                       Prob > F      =  0.0005
                                                       R-squared     =  0.4007
                                                       Root MSE      =  1.1956

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
fragmentat~n |  -.3472055   .1235835    -2.81   0.007    -.5945847   -.0998263
concentrat~n |  -.1966835   .2669845    -0.74   0.464    -.7311111     .337744
   frag_conc |   .2558319   .0837544     3.05   0.003     .0881793    .4234845
       _cons |   2.079945   .2306208     9.02   0.000     1.618308    2.541583
------------------------------------------------------------------------------

. *     ****************************************************************  *;
. *                               Model 3                                 *;
. *     ****************************************************************  *;
. regress legparties logmag10 proximity prescandidate prox_prescandidate fragmentation concentration frag_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  7,    54) =    8.88
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.6902
                                                       Root MSE      =  .89081

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    logmag10 |   .3440826   .1241334     2.77   0.008     .0952101    .5929551
   proximity |  -.8342611    .455975    -1.83   0.073    -1.748436    .0799138
prescandid~e |   .5398099   .1876407     2.88   0.006      .163613    .9160069
prox_presc~e |  -.0293437   .2955283    -0.10   0.921    -.6218422    .5631549
fragmentat~n |  -.2506207    .097681    -2.57   0.013    -.4464592   -.0547821
concentrat~n |   -.535042   .1922578    -2.78   0.007    -.9204956   -.1495884
   frag_conc |   .2382385   .0607144     3.92   0.000     .1165135    .3599636
       _cons |   1.445652   .2751781     5.25   0.000     .8939535    1.997351
------------------------------------------------------------------------------

. *     ****************************************************************  *;
. *                               Model 4                                 *;
. *     ****************************************************************  *;
. regress legparties logmag10 proximity prescandidate prox_prescandidate fragmentation concentration
> frag_conc logmag10_frag logmag10_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F(  9,    52) =   13.93
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.7386
                                                       Root MSE      =  .83392

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    logmag10 |  -.4346081   .4946096    -0.88   0.384    -1.427115    .5578986
   proximity |  -.5900543   .4422131    -1.33   0.188     -1.47742    .2973111
prescandid~e |   .5226013   .1791659     2.92   0.005     .1630787    .8821239
prox_presc~e |   .0094736   .2755043     0.03   0.973    -.5433662    .5623133
fragmentat~n |   -.559639   .1457875    -3.84   0.000    -.8521829   -.2670951
concentrat~n |  -.3227664    .268194    -1.20   0.234     -.860937    .2154042
   frag_conc |   .2863439   .0532074     5.38   0.000     .1795755    .3931123
logmag10_f~g |   .2078551   .0590955     3.52   0.001     .0892713     .326439
logmag10_c~c |  -.1489653   .2493712    -0.60   0.553    -.6493652    .3514345
       _cons |   1.921449    .359343     5.35   0.000     1.200375    2.642524
------------------------------------------------------------------------------

. regress legparties logmag10 proximity prescandidate prox_prescandidate fragmentation concentration
> frag_conc logmag10_frag logmag10_conc logmag10_frag_conc, robust;

Regression with robust standard errors                 Number of obs =      62
                                                       F( 10,    51) =   18.12
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.7566
                                                       Root MSE      =  .81255

------------------------------------------------------------------------------
             |               Robust
  legparties |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    logmag10 |   .6576745   .4536982     1.45   0.153    -.2531626    1.568512
   proximity |  -.5792874   .4341616    -1.33   0.188    -1.450903    .2923284
prescandid~e |   .4972876   .1789268     2.78   0.008      .138077    .8564981
prox_presc~e |   .0357922   .2703231     0.13   0.895     -.506904    .5784883
fragmentat~n |  -.3061517   .1244102    -2.46   0.017    -.5559156   -.0563877
concentrat~n |   .1077863   .2720849     0.40   0.694    -.4384468    .6540195
   frag_conc |   .1468926   .0486345     3.02   0.004     .0492546    .2445305
logmag10_f~g |   -.072974   .1039821    -0.70   0.486    -.2817268    .1357788
logmag10_c~c |  -.8640806   .3508728    -2.46   0.017    -1.568487    -.159674
~0_frag_conc |     .17936   .0804572     2.23   0.030     .0178355    .3408846
       _cons |   1.274294   .3003618     4.24   0.000     .6712925    1.877295
------------------------------------------------------------------------------

. *     ****************************************************************  *;
. *                       End of basic replication                        *;
. *     ****************************************************************  *;
. log close;
       log:  Z:\interactionmodels\replication\legislativeparties_replication.log
  log type:  text
 closed on:  10 Jan 2007, 20:00:49
-----------------------------------------------------------------------------------------------------------------------
